Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations100000
Missing cells666814
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 MiB
Average record size in memory160.0 B

Variable types

Text2
Categorical3
Numeric13
Boolean1

Alerts

MRG has constant value "False"Constant
ARPU_SEGMENT is highly overall correlated with FREQUENCE and 7 other fieldsHigh correlation
CHURN is highly overall correlated with REGULARITYHigh correlation
FREQUENCE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
FREQUENCE_RECH is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
FREQ_TOP_PACK is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
MONTANT is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
ON_NET is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
ORANGE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
REGULARITY is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
REVENUE is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
TENURE is highly imbalanced (86.3%)Imbalance
REGION has 39388 (39.4%) missing valuesMissing
MONTANT has 35036 (35.0%) missing valuesMissing
FREQUENCE_RECH has 35036 (35.0%) missing valuesMissing
REVENUE has 33688 (33.7%) missing valuesMissing
ARPU_SEGMENT has 33688 (33.7%) missing valuesMissing
FREQUENCE has 33688 (33.7%) missing valuesMissing
DATA_VOLUME has 49213 (49.2%) missing valuesMissing
ON_NET has 36402 (36.4%) missing valuesMissing
ORANGE has 41466 (41.5%) missing valuesMissing
TIGO has 59684 (59.7%) missing valuesMissing
ZONE1 has 92192 (92.2%) missing valuesMissing
ZONE2 has 93709 (93.7%) missing valuesMissing
TOP_PACK has 41812 (41.8%) missing valuesMissing
FREQ_TOP_PACK has 41812 (41.8%) missing valuesMissing
DATA_VOLUME is highly skewed (γ1 = 24.68954921)Skewed
ZONE2 is highly skewed (γ1 = 22.23567431)Skewed
user_id has unique valuesUnique
DATA_VOLUME has 14885 (14.9%) zerosZeros
ON_NET has 4926 (4.9%) zerosZeros
ORANGE has 2917 (2.9%) zerosZeros
TIGO has 4446 (4.4%) zerosZeros
ZONE1 has 2847 (2.8%) zerosZeros
ZONE2 has 1909 (1.9%) zerosZeros

Reproduction

Analysis started2025-11-19 07:06:24.156757
Analysis finished2025-11-19 07:07:05.491524
Duration41.33 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:06.136949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters4000000
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100000 ?
Unique (%)100.0%

Sample

1st row4361fb4fad11445ce7ec1ee054f41de5deba6fef
2nd row49c7c0d15acb8c92f9f4d8d3342385e996a9d292
3rd row23b4832c1ebee58bfdd2cc0c3886523d93f03c97
4th row7d0e2093cc4ca7fa9c50d68b539922a26a85306e
5th row3eac0fdfcf454d2857f26b6d41479033b7bb7366
ValueCountFrequency (%)
ac8b59acbf82e9b46c790be8c14886b504d1e1bf1
 
< 0.1%
2dd213c18d122ee0530c942762dc097b0bc89ea61
 
< 0.1%
60b73fc88e360b1f8641ccf32f75dc019080d2ba1
 
< 0.1%
c521ee8f2fd3bee1bb74543e2b89713352e50fd91
 
< 0.1%
7bea0b27bc9be8dcd57ea03187cbf3b3b96ae5b51
 
< 0.1%
fe00ea2f1e42db1003b5d191059658e2943837521
 
< 0.1%
c3aab75c6b7be051c7cd6582e9b484101644f5481
 
< 0.1%
64243eae2e904f8dc20a60a744cb885d945402571
 
< 0.1%
22615e522724aafb81ca67e11643c102e682f7401
 
< 0.1%
c6452d84050d34261da482c4d2d4dba71a67a7951
 
< 0.1%
Other values (99990)99990
> 99.9%
2025-11-19T10:07:06.727383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2251280
 
6.3%
d250555
 
6.3%
6250417
 
6.3%
0250259
 
6.3%
7250232
 
6.3%
9250033
 
6.3%
a250028
 
6.3%
3249980
 
6.2%
8249951
 
6.2%
c249903
 
6.2%
Other values (6)1497362
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2251280
 
6.3%
d250555
 
6.3%
6250417
 
6.3%
0250259
 
6.3%
7250232
 
6.3%
9250033
 
6.3%
a250028
 
6.3%
3249980
 
6.2%
8249951
 
6.2%
c249903
 
6.2%
Other values (6)1497362
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2251280
 
6.3%
d250555
 
6.3%
6250417
 
6.3%
0250259
 
6.3%
7250232
 
6.3%
9250033
 
6.3%
a250028
 
6.3%
3249980
 
6.2%
8249951
 
6.2%
c249903
 
6.2%
Other values (6)1497362
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2251280
 
6.3%
d250555
 
6.3%
6250417
 
6.3%
0250259
 
6.3%
7250232
 
6.3%
9250033
 
6.3%
a250028
 
6.3%
3249980
 
6.2%
8249951
 
6.2%
c249903
 
6.2%
Other values (6)1497362
37.4%

REGION
Categorical

Missing 

Distinct14
Distinct (%)< 0.1%
Missing39388
Missing (%)39.4%
Memory size1.5 MiB
DAKAR
23678 
THIES
8445 
SAINT-LOUIS
5468 
LOUGA
4749 
KAOLACK
4478 
Other values (9)
13794 

Length

Max length11
Median length5
Mean length6.3163565
Min length5

Characters and Unicode

Total characters382847
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAKAR
2nd rowSAINT-LOUIS
3rd rowKOLDA
4th rowTAMBACOUNDA
5th rowDAKAR

Common Values

ValueCountFrequency (%)
DAKAR23678
23.7%
THIES8445
 
8.4%
SAINT-LOUIS5468
 
5.5%
LOUGA4749
 
4.7%
KAOLACK4478
 
4.5%
DIOURBEL3180
 
3.2%
TAMBACOUNDA2524
 
2.5%
KAFFRINE2064
 
2.1%
KOLDA1849
 
1.8%
FATICK1637
 
1.6%
Other values (4)2540
 
2.5%
(Missing)39388
39.4%

Length

2025-11-19T10:07:06.864937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dakar23678
39.1%
thies8445
 
13.9%
saint-louis5468
 
9.0%
louga4749
 
7.8%
kaolack4478
 
7.4%
diourbel3180
 
5.2%
tambacounda2524
 
4.2%
kaffrine2064
 
3.4%
kolda1849
 
3.1%
fatick1637
 
2.7%
Other values (4)2540
 
4.2%

Most occurring characters

ValueCountFrequency (%)
A82341
21.5%
K38232
10.0%
D31402
 
8.2%
R29946
 
7.8%
I28433
 
7.4%
O23491
 
6.1%
L19724
 
5.2%
S19504
 
5.1%
T19419
 
5.1%
U17164
 
4.5%
Other values (10)73191
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)382847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A82341
21.5%
K38232
10.0%
D31402
 
8.2%
R29946
 
7.8%
I28433
 
7.4%
O23491
 
6.1%
L19724
 
5.2%
S19504
 
5.1%
T19419
 
5.1%
U17164
 
4.5%
Other values (10)73191
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)382847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A82341
21.5%
K38232
10.0%
D31402
 
8.2%
R29946
 
7.8%
I28433
 
7.4%
O23491
 
6.1%
L19724
 
5.2%
S19504
 
5.1%
T19419
 
5.1%
U17164
 
4.5%
Other values (10)73191
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)382847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A82341
21.5%
K38232
10.0%
D31402
 
8.2%
R29946
 
7.8%
I28433
 
7.4%
O23491
 
6.1%
L19724
 
5.2%
S19504
 
5.1%
T19419
 
5.1%
U17164
 
4.5%
Other values (10)73191
19.1%

TENURE
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
K > 24 month
94753 
I 18-21 month
 
2154
H 15-18 month
 
1244
G 12-15 month
 
707
J 21-24 month
 
627
Other values (3)
 
515

Length

Max length13
Median length12
Mean length12.04625
Min length11

Characters and Unicode

Total characters1204625
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK > 24 month
2nd rowK > 24 month
3rd rowK > 24 month
4th rowK > 24 month
5th rowK > 24 month

Common Values

ValueCountFrequency (%)
K > 24 month94753
94.8%
I 18-21 month2154
 
2.2%
H 15-18 month1244
 
1.2%
G 12-15 month707
 
0.7%
J 21-24 month627
 
0.6%
F 9-12 month408
 
0.4%
E 6-9 month71
 
0.1%
D 3-6 month36
 
< 0.1%

Length

2025-11-19T10:07:06.990899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T10:07:07.171965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
month100000
25.3%
k94753
24.0%
94753
24.0%
2494753
24.0%
i2154
 
0.5%
18-212154
 
0.5%
h1244
 
0.3%
15-181244
 
0.3%
g707
 
0.2%
12-15707
 
0.2%
Other values (8)2284
 
0.6%

Most occurring characters

ValueCountFrequency (%)
294753
24.5%
n100000
 
8.3%
o100000
 
8.3%
m100000
 
8.3%
h100000
 
8.3%
t100000
 
8.3%
299276
 
8.2%
495380
 
7.9%
K94753
 
7.9%
>94753
 
7.9%
Other values (14)25710
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1204625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
294753
24.5%
n100000
 
8.3%
o100000
 
8.3%
m100000
 
8.3%
h100000
 
8.3%
t100000
 
8.3%
299276
 
8.2%
495380
 
7.9%
K94753
 
7.9%
>94753
 
7.9%
Other values (14)25710
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1204625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
294753
24.5%
n100000
 
8.3%
o100000
 
8.3%
m100000
 
8.3%
h100000
 
8.3%
t100000
 
8.3%
299276
 
8.2%
495380
 
7.9%
K94753
 
7.9%
>94753
 
7.9%
Other values (14)25710
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1204625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
294753
24.5%
n100000
 
8.3%
o100000
 
8.3%
m100000
 
8.3%
h100000
 
8.3%
t100000
 
8.3%
299276
 
8.2%
495380
 
7.9%
K94753
 
7.9%
>94753
 
7.9%
Other values (14)25710
 
2.1%

MONTANT
Real number (ℝ)

High correlation  Missing 

Distinct1243
Distinct (%)1.9%
Missing35036
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean5472.2378
Minimum20
Maximum189300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:07.364779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile250
Q11000
median3000
Q37250
95-th percentile18500
Maximum189300
Range189280
Interquartile range (IQR)6250

Descriptive statistics

Standard deviation6889.3693
Coefficient of variation (CV)1.2589675
Kurtosis29.65455
Mean5472.2378
Median Absolute Deviation (MAD)2400
Skewness3.5300352
Sum3.5549845 × 108
Variance47463410
MonotonicityNot monotonic
2025-11-19T10:07:07.569070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5005197
 
5.2%
10003836
 
3.8%
15002324
 
2.3%
20002072
 
2.1%
2001938
 
1.9%
30001700
 
1.7%
25001522
 
1.5%
35001101
 
1.1%
40001077
 
1.1%
5000971
 
1.0%
Other values (1233)43226
43.2%
(Missing)35036
35.0%
ValueCountFrequency (%)
201
 
< 0.1%
301
 
< 0.1%
5012
 
< 0.1%
100914
0.9%
1141
 
< 0.1%
15098
 
0.1%
2001938
1.9%
2481
 
< 0.1%
250389
 
0.4%
300592
 
0.6%
ValueCountFrequency (%)
1893001
< 0.1%
1645001
< 0.1%
1235001
< 0.1%
1132001
< 0.1%
1109001
< 0.1%
1100001
< 0.1%
1080001
< 0.1%
1072501
< 0.1%
897001
< 0.1%
887001
< 0.1%

FREQUENCE_RECH
Real number (ℝ)

High correlation  Missing 

Distinct103
Distinct (%)0.2%
Missing35036
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean11.464242
Minimum1
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:07.837080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q315
95-th percentile39
Maximum114
Range113
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.179574
Coefficient of variation (CV)1.1496246
Kurtosis5.3665077
Mean11.464242
Median Absolute Deviation (MAD)5
Skewness2.1157598
Sum744763
Variance173.70118
MonotonicityNot monotonic
2025-11-19T10:07:08.030903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110194
 
10.2%
26536
 
6.5%
35255
 
5.3%
44111
 
4.1%
53425
 
3.4%
62979
 
3.0%
72524
 
2.5%
82330
 
2.3%
92033
 
2.0%
101901
 
1.9%
Other values (93)23676
23.7%
(Missing)35036
35.0%
ValueCountFrequency (%)
110194
10.2%
26536
6.5%
35255
5.3%
44111
4.1%
53425
 
3.4%
62979
 
3.0%
72524
 
2.5%
82330
 
2.3%
92033
 
2.0%
101901
 
1.9%
ValueCountFrequency (%)
1141
 
< 0.1%
1081
 
< 0.1%
1021
 
< 0.1%
1012
< 0.1%
1001
 
< 0.1%
993
< 0.1%
982
< 0.1%
961
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%

REVENUE
Real number (ℝ)

High correlation  Missing 

Distinct13505
Distinct (%)20.4%
Missing33688
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean5458.459
Minimum1
Maximum165166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:08.240648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile199
Q11000
median3000
Q37299
95-th percentile18680.45
Maximum165166
Range165165
Interquartile range (IQR)6299

Descriptive statistics

Standard deviation6932.9556
Coefficient of variation (CV)1.2701306
Kurtosis20.036382
Mean5458.459
Median Absolute Deviation (MAD)2495
Skewness3.1673442
Sum3.6196133 × 108
Variance48065874
MonotonicityNot monotonic
2025-11-19T10:07:08.497804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5002702
 
2.7%
10001658
 
1.7%
1500963
 
1.0%
200948
 
0.9%
2000801
 
0.8%
3000633
 
0.6%
2500589
 
0.6%
3500394
 
0.4%
4000391
 
0.4%
100359
 
0.4%
Other values (13495)56874
56.9%
(Missing)33688
33.7%
ValueCountFrequency (%)
1210
0.2%
2134
0.1%
38
 
< 0.1%
490
0.1%
58
 
< 0.1%
649
 
< 0.1%
725
 
< 0.1%
851
 
0.1%
960
 
0.1%
10124
0.1%
ValueCountFrequency (%)
1651661
< 0.1%
1157001
< 0.1%
1153281
< 0.1%
1144041
< 0.1%
989661
< 0.1%
971951
< 0.1%
946971
< 0.1%
863411
< 0.1%
859481
< 0.1%
849201
< 0.1%

ARPU_SEGMENT
Real number (ℝ)

High correlation  Missing 

Distinct7191
Distinct (%)10.8%
Missing33688
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean1819.4915
Minimum0
Maximum55055
Zeros210
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:08.694298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q1333
median1000
Q32433
95-th percentile6227.15
Maximum55055
Range55055
Interquartile range (IQR)2100

Descriptive statistics

Standard deviation2310.9829
Coefficient of variation (CV)1.2701257
Kurtosis20.03648
Mean1819.4915
Median Absolute Deviation (MAD)832
Skewness3.1673591
Sum1.2065412 × 108
Variance5340641.8
MonotonicityNot monotonic
2025-11-19T10:07:08.877100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1673097
 
3.1%
3331994
 
2.0%
5001323
 
1.3%
671067
 
1.1%
6671024
 
1.0%
1000883
 
0.9%
833745
 
0.7%
1167516
 
0.5%
1333502
 
0.5%
33493
 
0.5%
Other values (7181)54668
54.7%
(Missing)33688
33.7%
ValueCountFrequency (%)
0210
0.2%
1232
0.2%
282
 
0.1%
3235
0.2%
4128
0.1%
593
 
0.1%
654
 
0.1%
7135
0.1%
838
 
< 0.1%
955
 
0.1%
ValueCountFrequency (%)
550551
< 0.1%
385671
< 0.1%
384431
< 0.1%
381351
< 0.1%
329891
< 0.1%
323981
< 0.1%
315661
< 0.1%
287801
< 0.1%
286491
< 0.1%
283071
< 0.1%

FREQUENCE
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)0.1%
Missing33688
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean13.933195
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:09.086927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q319
95-th percentile45
Maximum91
Range90
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.611342
Coefficient of variation (CV)1.0486713
Kurtosis3.4442569
Mean13.933195
Median Absolute Deviation (MAD)7
Skewness1.778955
Sum923938
Variance213.49131
MonotonicityNot monotonic
2025-11-19T10:07:09.339780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17525
 
7.5%
25358
 
5.4%
34407
 
4.4%
43804
 
3.8%
53319
 
3.3%
63044
 
3.0%
72617
 
2.6%
82411
 
2.4%
92208
 
2.2%
102069
 
2.1%
Other values (81)29550
29.5%
(Missing)33688
33.7%
ValueCountFrequency (%)
17525
7.5%
25358
5.4%
34407
4.4%
43804
3.8%
53319
3.3%
63044
3.0%
72617
 
2.6%
82411
 
2.4%
92208
 
2.2%
102069
 
2.1%
ValueCountFrequency (%)
912
 
< 0.1%
903
 
< 0.1%
895
 
< 0.1%
885
 
< 0.1%
8713
< 0.1%
8618
< 0.1%
859
 
< 0.1%
8412
< 0.1%
8317
< 0.1%
8228
< 0.1%

DATA_VOLUME
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct11414
Distinct (%)22.5%
Missing49213
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean3289.8707
Minimum0
Maximum737388
Zeros14885
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:09.552860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median253
Q32911
95-th percentile14714
Maximum737388
Range737388
Interquartile range (IQR)2911

Descriptive statistics

Standard deviation11891.046
Coefficient of variation (CV)3.6144418
Kurtosis995.90842
Mean3289.8707
Median Absolute Deviation (MAD)253
Skewness24.689549
Sum1.6708266 × 108
Variance1.4139698 × 108
MonotonicityNot monotonic
2025-11-19T10:07:09.786808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014885
 
14.9%
11920
 
1.9%
2613
 
0.6%
3327
 
0.3%
4267
 
0.3%
1024252
 
0.3%
5238
 
0.2%
1023184
 
0.2%
6171
 
0.2%
7158
 
0.2%
Other values (11404)31772
31.8%
(Missing)49213
49.2%
ValueCountFrequency (%)
014885
14.9%
11920
 
1.9%
2613
 
0.6%
3327
 
0.3%
4267
 
0.3%
5238
 
0.2%
6171
 
0.2%
7158
 
0.2%
8114
 
0.1%
9120
 
0.1%
ValueCountFrequency (%)
7373881
< 0.1%
6360221
< 0.1%
5609331
< 0.1%
5103331
< 0.1%
4904581
< 0.1%
4670481
< 0.1%
4485281
< 0.1%
4345711
< 0.1%
4135801
< 0.1%
3953171
< 0.1%

ON_NET
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct3488
Distinct (%)5.5%
Missing36402
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean275.28594
Minimum0
Maximum36687
Zeros4926
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:09.990439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median27
Q3158
95-th percentile1355.15
Maximum36687
Range36687
Interquartile range (IQR)153

Descriptive statistics

Standard deviation858.22229
Coefficient of variation (CV)3.1175668
Kurtosis126.43157
Mean275.28594
Median Absolute Deviation (MAD)26
Skewness8.2102073
Sum17507635
Variance736545.5
MonotonicityNot monotonic
2025-11-19T10:07:10.169335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04926
 
4.9%
14379
 
4.4%
22653
 
2.7%
31938
 
1.9%
71907
 
1.9%
81799
 
1.8%
41775
 
1.8%
51481
 
1.5%
61383
 
1.4%
9950
 
0.9%
Other values (3478)40407
40.4%
(Missing)36402
36.4%
ValueCountFrequency (%)
04926
4.9%
14379
4.4%
22653
2.7%
31938
 
1.9%
41775
 
1.8%
51481
 
1.5%
61383
 
1.4%
71907
 
1.9%
81799
 
1.8%
9950
 
0.9%
ValueCountFrequency (%)
366871
< 0.1%
208371
< 0.1%
195861
< 0.1%
190741
< 0.1%
184851
< 0.1%
169271
< 0.1%
162161
< 0.1%
160331
< 0.1%
154671
< 0.1%
153511
< 0.1%

ORANGE
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1349
Distinct (%)2.3%
Missing41466
Missing (%)41.5%
Infinite0
Infinite (%)0.0%
Mean95.981686
Minimum0
Maximum4743
Zeros2917
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:10.401628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median29
Q399
95-th percentile394
Maximum4743
Range4743
Interquartile range (IQR)92

Descriptive statistics

Standard deviation206.35782
Coefficient of variation (CV)2.1499708
Kurtosis80.690765
Mean95.981686
Median Absolute Deviation (MAD)27
Skewness6.9749564
Sum5618192
Variance42583.549
MonotonicityNot monotonic
2025-11-19T10:07:10.567403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13217
 
3.2%
02917
 
2.9%
22351
 
2.4%
31645
 
1.6%
41524
 
1.5%
51202
 
1.2%
81155
 
1.2%
61034
 
1.0%
7989
 
1.0%
10953
 
1.0%
Other values (1339)41547
41.5%
(Missing)41466
41.5%
ValueCountFrequency (%)
02917
2.9%
13217
3.2%
22351
2.4%
31645
1.6%
41524
1.5%
51202
 
1.2%
61034
 
1.0%
7989
 
1.0%
81155
 
1.2%
9919
 
0.9%
ValueCountFrequency (%)
47431
< 0.1%
45251
< 0.1%
44841
< 0.1%
44301
< 0.1%
42731
< 0.1%
41871
< 0.1%
40501
< 0.1%
40411
< 0.1%
40271
< 0.1%
39301
< 0.1%

TIGO
Real number (ℝ)

Missing  Zeros 

Distinct488
Distinct (%)1.2%
Missing59684
Missing (%)59.7%
Infinite0
Infinite (%)0.0%
Mean22.627766
Minimum0
Maximum1739
Zeros4446
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:10.783653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q320
95-th percentile93
Maximum1739
Range1739
Interquartile range (IQR)18

Descriptive statistics

Standard deviation58.328777
Coefficient of variation (CV)2.5777524
Kurtosis176.32164
Mean22.627766
Median Absolute Deviation (MAD)5
Skewness10.145842
Sum912261
Variance3402.2462
MonotonicityNot monotonic
2025-11-19T10:07:10.998555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15237
 
5.2%
04446
 
4.4%
23348
 
3.3%
32433
 
2.4%
41957
 
2.0%
51612
 
1.6%
61381
 
1.4%
71251
 
1.3%
81091
 
1.1%
91013
 
1.0%
Other values (478)16547
 
16.5%
(Missing)59684
59.7%
ValueCountFrequency (%)
04446
4.4%
15237
5.2%
23348
3.3%
32433
2.4%
41957
 
2.0%
51612
 
1.6%
61381
 
1.4%
71251
 
1.3%
81091
 
1.1%
91013
 
1.0%
ValueCountFrequency (%)
17391
< 0.1%
16101
< 0.1%
16011
< 0.1%
15271
< 0.1%
15021
< 0.1%
14711
< 0.1%
14661
< 0.1%
14351
< 0.1%
13721
< 0.1%
13621
< 0.1%

ZONE1
Real number (ℝ)

Missing  Zeros 

Distinct180
Distinct (%)2.3%
Missing92192
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean7.5633965
Minimum0
Maximum1274
Zeros2847
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:11.208988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile32
Maximum1274
Range1274
Interquartile range (IQR)3

Descriptive statistics

Standard deviation32.718917
Coefficient of variation (CV)4.3259556
Kurtosis368.819
Mean7.5633965
Median Absolute Deviation (MAD)1
Skewness14.477113
Sum59055
Variance1070.5276
MonotonicityNot monotonic
2025-11-19T10:07:11.397206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02847
 
2.8%
11875
 
1.9%
2771
 
0.8%
3442
 
0.4%
4284
 
0.3%
5187
 
0.2%
6131
 
0.1%
7121
 
0.1%
895
 
0.1%
985
 
0.1%
Other values (170)970
 
1.0%
(Missing)92192
92.2%
ValueCountFrequency (%)
02847
2.8%
11875
1.9%
2771
 
0.8%
3442
 
0.4%
4284
 
0.3%
5187
 
0.2%
6131
 
0.1%
7121
 
0.1%
895
 
0.1%
985
 
0.1%
ValueCountFrequency (%)
12741
< 0.1%
5971
< 0.1%
5801
< 0.1%
5441
< 0.1%
5231
< 0.1%
4481
< 0.1%
4211
< 0.1%
4131
< 0.1%
4071
< 0.1%
3801
< 0.1%

ZONE2
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct140
Distinct (%)2.2%
Missing93709
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean7.587188
Minimum0
Maximum1346
Zeros1909
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:11.599413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile29
Maximum1346
Range1346
Interquartile range (IQR)5

Descriptive statistics

Standard deviation35.586052
Coefficient of variation (CV)4.690282
Kurtosis693.99864
Mean7.587188
Median Absolute Deviation (MAD)1
Skewness22.235674
Sum47731
Variance1266.3671
MonotonicityNot monotonic
2025-11-19T10:07:11.774866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01909
 
1.9%
11238
 
1.2%
2708
 
0.7%
3449
 
0.4%
4319
 
0.3%
5222
 
0.2%
6158
 
0.2%
7156
 
0.2%
8111
 
0.1%
997
 
0.1%
Other values (130)924
 
0.9%
(Missing)93709
93.7%
ValueCountFrequency (%)
01909
1.9%
11238
1.2%
2708
 
0.7%
3449
 
0.4%
4319
 
0.3%
5222
 
0.2%
6158
 
0.2%
7156
 
0.2%
8111
 
0.1%
997
 
0.1%
ValueCountFrequency (%)
13461
< 0.1%
13161
< 0.1%
7201
< 0.1%
6851
< 0.1%
5931
< 0.1%
5151
< 0.1%
5031
< 0.1%
4871
< 0.1%
4271
< 0.1%
3681
< 0.1%

MRG
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.9 KiB
False
100000 
ValueCountFrequency (%)
False100000
100.0%
2025-11-19T10:07:11.877457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

REGULARITY
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.05076
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:12.018470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median24
Q351
95-th percentile62
Maximum62
Range61
Interquartile range (IQR)45

Descriptive statistics

Standard deviation22.280221
Coefficient of variation (CV)0.79428227
Kurtosis-1.487114
Mean28.05076
Median Absolute Deviation (MAD)20
Skewness0.24528904
Sum2805076
Variance496.40827
MonotonicityNot monotonic
2025-11-19T10:07:12.386887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19266
 
9.3%
627575
 
7.6%
25477
 
5.5%
34001
 
4.0%
43118
 
3.1%
613045
 
3.0%
52582
 
2.6%
602218
 
2.2%
62180
 
2.2%
72041
 
2.0%
Other values (52)58497
58.5%
ValueCountFrequency (%)
19266
9.3%
25477
5.5%
34001
4.0%
43118
 
3.1%
52582
 
2.6%
62180
 
2.2%
72041
 
2.0%
81893
 
1.9%
91724
 
1.7%
101641
 
1.6%
ValueCountFrequency (%)
627575
7.6%
613045
3.0%
602218
 
2.2%
591858
 
1.9%
581659
 
1.7%
571471
 
1.5%
561383
 
1.4%
551235
 
1.2%
541284
 
1.3%
531195
 
1.2%

TOP_PACK
Text

Missing 

Distinct88
Distinct (%)0.2%
Missing41812
Missing (%)41.8%
Memory size1.5 MiB
2025-11-19T10:07:12.678991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length42
Mean length23.213996
Min length9

Characters and Unicode

Total characters1350776
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowOn net 200F=Unlimited _call24H
2nd rowOn-net 500=4000,10d
3rd rowOn net 200F=Unlimited _call24H
4th rowData: 100 F=40MB,24H
5th rowData: 100 F=40MB,24H
ValueCountFrequency (%)
all-net18013
 
12.4%
500f=2000f;5d14788
 
10.1%
net12141
 
8.3%
on11232
 
7.7%
200f=unlimited7183
 
4.9%
call24h7183
 
4.9%
2500f6096
 
4.2%
data5953
 
4.1%
data:490f=1gb,7d5330
 
3.7%
mixt4281
 
2.9%
Other values (123)53585
36.8%
2025-11-19T10:07:13.115120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0190277
 
14.1%
87597
 
6.5%
l81947
 
6.1%
F78570
 
5.8%
t75189
 
5.6%
n68093
 
5.0%
263352
 
4.7%
e54693
 
4.0%
a51758
 
3.8%
551593
 
3.8%
Other values (60)547707
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1350776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0190277
 
14.1%
87597
 
6.5%
l81947
 
6.1%
F78570
 
5.8%
t75189
 
5.6%
n68093
 
5.0%
263352
 
4.7%
e54693
 
4.0%
a51758
 
3.8%
551593
 
3.8%
Other values (60)547707
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1350776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0190277
 
14.1%
87597
 
6.5%
l81947
 
6.1%
F78570
 
5.8%
t75189
 
5.6%
n68093
 
5.0%
263352
 
4.7%
e54693
 
4.0%
a51758
 
3.8%
551593
 
3.8%
Other values (60)547707
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1350776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0190277
 
14.1%
87597
 
6.5%
l81947
 
6.1%
F78570
 
5.8%
t75189
 
5.6%
n68093
 
5.0%
263352
 
4.7%
e54693
 
4.0%
a51758
 
3.8%
551593
 
3.8%
Other values (60)547707
40.5%

FREQ_TOP_PACK
Real number (ℝ)

High correlation  Missing 

Distinct127
Distinct (%)0.2%
Missing41812
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean9.2166942
Minimum1
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-19T10:07:13.288370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile33
Maximum320
Range319
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.019911
Coefficient of variation (CV)1.3041456
Kurtosis22.215994
Mean9.2166942
Median Absolute Deviation (MAD)4
Skewness3.3071448
Sum536301
Variance144.47826
MonotonicityNot monotonic
2025-11-19T10:07:13.516354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111685
 
11.7%
27324
 
7.3%
35411
 
5.4%
43949
 
3.9%
53077
 
3.1%
62648
 
2.6%
72395
 
2.4%
82042
 
2.0%
91806
 
1.8%
101598
 
1.6%
Other values (117)16253
 
16.3%
(Missing)41812
41.8%
ValueCountFrequency (%)
111685
11.7%
27324
7.3%
35411
5.4%
43949
 
3.9%
53077
 
3.1%
62648
 
2.6%
72395
 
2.4%
82042
 
2.0%
91806
 
1.8%
101598
 
1.6%
ValueCountFrequency (%)
3201
< 0.1%
2121
< 0.1%
2001
< 0.1%
1561
< 0.1%
1391
< 0.1%
1361
< 0.1%
1341
< 0.1%
1331
< 0.1%
1291
< 0.1%
1271
< 0.1%

CHURN
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
81298 
1
18702 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
081298
81.3%
118702
 
18.7%

Length

2025-11-19T10:07:13.721722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T10:07:13.849896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
081298
81.3%
118702
 
18.7%

Most occurring characters

ValueCountFrequency (%)
081298
81.3%
118702
 
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
081298
81.3%
118702
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
081298
81.3%
118702
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
081298
81.3%
118702
 
18.7%

Interactions

2025-11-19T10:07:00.894325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-11-19T10:07:13.957359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ARPU_SEGMENTCHURNDATA_VOLUMEFREQUENCEFREQUENCE_RECHFREQ_TOP_PACKMONTANTON_NETORANGEREGIONREGULARITYREVENUETENURETIGOZONE1ZONE2
ARPU_SEGMENT1.0000.0510.3840.8800.8800.8180.9870.5190.6820.0320.7151.0000.0210.4570.2240.320
CHURN0.0511.0000.0000.1470.1160.0370.0380.0220.0260.0280.5610.0510.0450.0100.0000.023
DATA_VOLUME0.3840.0001.0000.3260.2940.2250.374-0.102-0.0220.0120.2990.3840.048-0.012-0.027-0.001
FREQUENCE0.8800.1470.3261.0000.9510.8680.8700.4370.5300.0520.6900.8800.0080.3380.0870.201
FREQUENCE_RECH0.8800.1160.2940.9511.0000.8950.8870.4770.5640.0470.6790.8800.0050.3630.0890.193
FREQ_TOP_PACK0.8180.0370.2250.8680.8951.0000.8130.4390.5390.0260.5960.8180.0000.3520.0970.076
MONTANT0.9870.0380.3740.8700.8870.8131.0000.5090.6710.0280.7060.9870.0220.4530.2190.315
ON_NET0.5190.022-0.1020.4370.4770.4390.5091.0000.5540.0120.5210.5190.0000.3650.080-0.011
ORANGE0.6820.026-0.0220.5300.5640.5390.6710.5541.0000.0220.4600.6820.0000.4730.1320.066
REGION0.0320.0280.0120.0520.0470.0260.0280.0120.0221.0000.0340.0320.0240.0070.0060.000
REGULARITY0.7150.5610.2990.6900.6790.5960.7060.5210.4600.0341.0000.7150.0150.3240.0600.051
REVENUE1.0000.0510.3840.8800.8800.8180.9870.5190.6820.0320.7151.0000.0210.4570.2240.320
TENURE0.0210.0450.0480.0080.0050.0000.0220.0000.0000.0240.0150.0211.0000.0100.0000.135
TIGO0.4570.010-0.0120.3380.3630.3520.4530.3650.4730.0070.3240.4570.0101.0000.0730.022
ZONE10.2240.000-0.0270.0870.0890.0970.2190.0800.1320.0060.0600.2240.0000.0731.0000.089
ZONE20.3200.023-0.0010.2010.1930.0760.315-0.0110.0660.0000.0510.3200.1350.0220.0891.000

Missing values

2025-11-19T10:07:03.247577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-19T10:07:03.655895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-19T10:07:04.847365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
5669394361fb4fad11445ce7ec1ee054f41de5deba6fefDAKARK > 24 month4200.08.04199.01400.014.01.0314.0132.0NaNNaNNaNNO20On net 200F=Unlimited _call24H3.00
62079649c7c0d15acb8c92f9f4d8d3342385e996a9d292SAINT-LOUISK > 24 month1000.02.01000.0333.02.0NaN59.03.0NaNNaNNaNNO17On-net 500=4000,10d1.00
29970323b4832c1ebee58bfdd2cc0c3886523d93f03c97NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO2NaNNaN1
10514947d0e2093cc4ca7fa9c50d68b539922a26a85306eKOLDAK > 24 month5300.015.05150.01717.018.0NaN1482.088.0NaNNaNNaNNO50On net 200F=Unlimited _call24H7.00
5273533eac0fdfcf454d2857f26b6d41479033b7bb7366TAMBACOUNDAK > 24 month200.01.0399.0133.03.032.07.00.0NaNNaNNaNNO39Data: 100 F=40MB,24H1.00
187702165f5bd67fcc3ed031fb1ae50ccb90c01c1ff339DAKARK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO16NaNNaN0
54464068e09f016f74ad22f99b9497522c2c5579f2570MATAMK > 24 month1100.07.01101.0367.07.082.01.01.0NaNNaNNaNNO38Data: 100 F=40MB,24H3.00
5099763c9ebe75b00b7c98d8c2ded26b8f8575f268610aSAINT-LOUISK > 24 month2050.015.01984.0661.016.0NaN5.05.03.0NaNNaNNO49NaNNaN0
1607660bf0c3bad13eaf507899eace9553b2abba18c5056NaNK > 24 monthNaNNaN20.07.02.0NaNNaNNaNNaNNaNNaNNO19NaNNaN0
2291831b4fae58ab8265065cf8fad756403468c5849b35NaNK > 24 month6000.02.05670.01890.03.01094.0NaN77.03.0NaN2.0NO10All-net 1000=5000;5d1.01
user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
1646772c3aab75c6b7be051c7cd6582e9b484101644f548THIESK > 24 month5700.012.06146.02049.016.0NaN139.089.010.0NaNNaNNO53All-net 500F=2000F;5d10.00
84216164243eae2e904f8dc20a60a744cb885d94540257THIESK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO15NaNNaN0
28856622615e522724aafb81ca67e11643c102e682f740MATAMK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO11NaNNaN0
1668519c6452d84050d34261da482c4d2d4dba71a67a795LOUGAK > 24 month7000.011.07008.02336.017.015755.01.044.0NaN0.00.0NO62Data:490F=1GB,7d7.00
6401534c153b4b4f20e940818777dfb4c257d0f2a183e5NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN1
1773205d2bb40ff9411348ccfcd49b2ee69d6714c3a5448NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN1
6719414fd85ca8877d2236dd50cdaca81648eaaf603769NaNG 12-15 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN1
6543294dc4972bd9a36a0fd32d69c9b0c28814478146fbDAKARK > 24 month6200.08.06190.02063.010.012529.0265.097.02.0NaNNaNNO48Data:1000F=2GB,30d4.00
860550a48f6f865c3260613e81c99d24e1a5c554462ecSAINT-LOUISK > 24 month500.01.0348.0116.03.0NaNNaN2.00.0NaNNaNNO4NaNNaN0
1986480ec19bf149fb182aab27b75e9f6d0ac3ec118c9caZIGUINCHORK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO3NaNNaN0